An effective method for real-time pedestrian detection applied to far-infrared images is presented, which makes use of the characteristics of pedestrian regions in far-infrared images and is based on a hybrid classification features algorithm. First, two levels of statistical adaptive oriented projection methods based on the high brightness property of the pedestrian pixels are used to locate the Regions of Interest (ROI) and eliminate the “shadow” phenomenon caused by one level oriented projection method. Then the method combines the pedestrian’s shape-dependent and shape-independent features (including shape’s morphological feature, inertia-based feature and histograms of oriented gradients (HOG) feature) to describe the ROI in the round. Finally, Support Vector Machine (SVM) is applied to classify and detect the pedestrian region. Experimental results of several far-infrared image sequences show that the proposed method achieves highly accurate pedestrian detection by combining hybrid classification features, and can be employed in real-time applications.